
"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor



class 	__a	(   unittest.TestCase						):






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]=7							,   UpperCAmelCase_			:				List[Any]=3							,   UpperCAmelCase_			:				Optional[int]=30							,   UpperCAmelCase_			:				Any=400							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Dict=None							,   UpperCAmelCase_			:				Dict=0.9							,   UpperCAmelCase_			:				Tuple=None							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Optional[int]=[0.5, 0.5, 0.5]							,   UpperCAmelCase_			:				Union[str, Any]=[0.5, 0.5, 0.5]							,   )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		size if size is not None else {"shortest_edge": 30}
              UpperCamelCase       			=		crop_size if crop_size is not None else {"height": 30, "width": 30}
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		min_resolution
              UpperCamelCase       			=		max_resolution
              UpperCamelCase       			=		do_resize_and_center_crop
              UpperCamelCase       			=		size
              UpperCamelCase       			=		crop_pct
              UpperCamelCase       			=		crop_size
              UpperCamelCase       			=		do_normalize
              UpperCamelCase       			=		image_mean
              UpperCamelCase       			=		image_std






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }





@require_torch
@require_vision
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       List[Any]				=      PoolFormerImageProcessor if is_vision_available() else None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		PoolFormerImageProcessingTester(self      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_resize_and_center_crop"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "size"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "crop_pct"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_normalize"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_mean"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_std"      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 30}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 30, "width": 30}      )

              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							,   crop_size=84      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 42}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 84, "width": 84}      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   Image.Image      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values

              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[str]:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   numpify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   np.ndarray      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	str:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   torchify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   torch.Tensor      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )







"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Any				=      '''deberta-v2'''






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=128_100							,   UpperCAmelCase_			:				List[str]=1_536							,   UpperCAmelCase_			:				List[str]=24							,   UpperCAmelCase_			:				int=24							,   UpperCAmelCase_			:				int=6_144							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				int=0.1							,   UpperCAmelCase_			:				Optional[int]=512							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				Optional[Any]=1e-7							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Tuple=-1							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Optional[Any]=0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   **UpperCAmelCase_			:				Optional[int]							,   )->	int:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		max_relative_positions
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		position_biased_input

              # Backwards compatibility
              if type(UpperCAmelCase_      ) == str:
                     UpperCamelCase       			=		[x.strip() for x in pos_att_type.lower().split("|"      )]

              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		layer_norm_eps

              UpperCamelCase       			=		kwargs.get("pooler_hidden_size"							,   UpperCAmelCase_      )
              UpperCamelCase       			=		pooler_dropout
              UpperCamelCase       			=		pooler_hidden_act





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]      )
              else:
                     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              return 12






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional["TensorType"] = None							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase" = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(preprocessor=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs





